Package com.heatonresearch.aifh.examples.optimization

Source Code of com.heatonresearch.aifh.examples.optimization.LearnIrisNelderMead

/*
* Artificial Intelligence for Humans
* Volume 1: Fundamental Algorithms
* Java Version
* http://www.aifh.org
* http://www.jeffheaton.com
*
* Code repository:
* https://github.com/jeffheaton/aifh

* Copyright 2013 by Jeff Heaton
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*     http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/

package com.heatonresearch.aifh.examples.optimization;

import com.heatonresearch.aifh.examples.learning.SimpleLearn;
import com.heatonresearch.aifh.general.data.BasicData;
import com.heatonresearch.aifh.learning.RBFNetwork;
import com.heatonresearch.aifh.learning.TrainNelderMead;
import com.heatonresearch.aifh.learning.score.ScoreFunction;
import com.heatonresearch.aifh.learning.score.ScoreRegressionData;
import com.heatonresearch.aifh.normalize.DataSet;
import com.heatonresearch.aifh.randomize.MersenneTwisterGenerateRandom;

import java.io.InputStream;
import java.util.List;
import java.util.Map;

/**
* Use a RBF network to learn the Iris data set, trained by Nelder Mead.
*/
public class LearnIrisNelderMead extends SimpleLearn {
    /**
     * Run the example.
     */
    public void process() {
        try {
            final InputStream istream = this.getClass().getResourceAsStream("/iris.csv");
            if( istream==null ) {
                System.out.println("Cannot access data set, make sure the resources are available.");
                System.exit(1);
            }
            final DataSet ds = DataSet.load(istream);
            // The following ranges are setup for the Iris data set.  If you wish to normalize other files you will
            // need to modify the below function calls other files.
            ds.normalizeRange(0, 0, 1);
            ds.normalizeRange(1, 0, 1);
            ds.normalizeRange(2, 0, 1);
            ds.normalizeRange(3, 0, 1);
            final Map<String, Integer> species = ds.encodeEquilateral(4);
            istream.close();

            final List<BasicData> trainingData = ds.extractSupervised(0, 4, 4, 2);

            final RBFNetwork network = new RBFNetwork(4, 4, 2);
            network.reset(new MersenneTwisterGenerateRandom());
            final ScoreFunction score = new ScoreRegressionData(trainingData);
            final TrainNelderMead train = new TrainNelderMead(network, score);
            performIterations(train, 1000, 0.01, true);
            queryEquilateral(network, trainingData, species, 0, 1);


        } catch (Throwable t) {
            t.printStackTrace();
        }


    }

    /**
     * The main method.
     *
     * @param args Not used.
     */
    public static void main(final String[] args) {
        final LearnIrisNelderMead prg = new LearnIrisNelderMead();
        prg.process();
    }
}
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